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"Kumar, Manish"
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2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors
Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.
Journal Article
Artificial Intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques
Annotation Build next-generation Artificial Intelligence systems with JavaKey FeaturesImplement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlibCreate self-learning systems using neural networks, NLP, and reinforcement learningBook DescriptionIn this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.What you will learnManage Artificial Intelligence techniques for big data with JavaBuild smart systems to analyze data for enhanced customer experienceLearn to use Artificial Intelligence frameworks for big dataUnderstand complex problems with algorithms and Neuro-Fuzzy systemsDesign stratagems to leverage data using Machine Learning processApply Deep Learning techniques to prepare data for modelingConstruct models that learn from data using open source toolsAnalyze big data problems using scalable Machine Learning algorithmsWho this book is forThis book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus
Book
Liquid crystals in photovoltaics: a new generation of organic photovoltaics
This article presents an overview of the developments in the field of organic photovoltaics (PVs) with liquid crystals (LCs). A brief introduction to the PV and LC fields is given first, followed by application of various LCs in organic PVs. Details of LCs used in bilayer solar cells, bulk heterojunction solar cells and dye-sensitized solar cells have been given. All the liquid crystalline materials used in PVs are structured and the efficiency of solar cells is tabulated. Finally, an outlook into the future of this newly emerging, fascinating and exciting field of self-organizing supramolecular LC PV research is provided.
Liquid crystals (LCs) have recently gained significant importance in organic photovoltaics (PVs). Power-conversion efficiency up to about 10% has reached in solar cells incorporating LCs. This review presents an overview of the developments in the field of organic PVs with LCs. Comprehensive details of LCs used in bilayer solar cells, bulk heterojunction solar cells and dye-sensitized solar cells have been given. An outlook into the future of this newly emerging, fascinating and exciting field of self-organizing supramolecular LC PV research is provided.
Journal Article
Anticancer potential of rhizome extract and a labdane diterpenoid from Curcuma mutabilis plant endemic to Western Ghats of India
Zingiberaceae plants are well known for their use in ethnomedicine.
Curcuma mutabilis
Škorničk., M. Sabu & Prasanthk., is an endemic Zingiberaceae species from Western Ghats of Kerala, India. Here, we report for the first time, the anticancer potential of petroleum ether extract from
C. mutabilis
rhizome (CMRP) and a novel labdane diterpenoid, (
E
)-14, 15-epoxylabda-8(17), 12-dien-16-al (Cm epoxide) isolated from it. CMRP was found to be a mixture of potent bioactive compounds including Cm epoxide. Both the extract and the compound displayed superior antiproliferative activity against several human cancer cell lines, without any display of cytotoxicity towards normal human cells such as peripheral blood derived lymphocytes and erythrocytes. CMRP treatment resulted in phosphatidylserine externalization, increase in the levels of intracellular ROS, Ca
2+
, loss of mitochondrial membrane potential as well as fragmentation of genomic DNA. Analyses of transcript profiling and immunostained western blots of extract-treated cancer cells confirmed induction of apoptosis by both intrinsic and extrinsic pathways. The purified compound, Cm epoxide, was also found to induce apoptosis in many human cancer cell types tested. Both CMRP and the Cm epoxide were found to be pharmacologically safe in terms of acute toxicity assessment using Swiss albino mice model. Further, molecular docking interactions of Cm epoxide with selected proteins involved in cell survival and death were also indicative of its druggability. Overall, our findings reveal that the endemic
C. mutabilis
rhizome extract and the compound Cm epoxide isolated from it are potential candidates for development of future cancer chemotherapeutics.
Journal Article
Species richness, phylogenetic diversity and phylogenetic structure patterns of exotic and native plants along an elevational gradient in the Himalaya
BackgroundSo far, macroecological studies in the Himalaya have mostly concentrated on spatial variation of overall species richness along the elevational gradient. Very few studies have attempted to document the difference in elevational richness patterns of native and exotic species. In this study, this knowledge gap is addressed by integrating data on phylogeny and elevational distribution of species to identify the variation in species richness, phylogenetic diversity and phylogenetic structure of exotic and native plant species along an elevational gradient in the Himalaya.ResultsSpecies distribution patterns for exotic and native species differed; exotics tended to show maximum species richness at low elevations while natives tended to predominate at mid-elevations. Native species assemblages showed higher phylogenetic diversity than the exotic species assemblages over the entire elevational gradient in the Himalaya. In terms of phylogenetic structure, exotic species assemblages showed majorly phylogenetic clustering while native species assemblages were characterized by phylogenetic overdispersion over the entire gradient.ConclusionsThe findings of this study indicate that areas with high native species richness and phylogenetic diversity are less receptive to exotic species and vice versa in the Himalaya. Species assemblages with high native phylogenetic overdispersion are less receptive to exotic species than the phylogenetically clustered assemblages. Different ecological processes (ecological filtering in case of exotics and resource and niche competition in case of natives) may govern the distribution of exotic and native species along the elevational gradient in the Himalaya.
Journal Article
Octonion quadratic-phase Fourier transform: inequalities, uncertainty principles, and examples
In this article, we define the octonion quadratic-phase Fourier transform (OQPFT) and derive its inversion formula, including its fundamental properties such as linearity, parity, modulation, and shifting. We also establish its relationship with the quaternion quadratic-phase Fourier transform (QQPFT). Further, we derive the Parseval formula and the Riemann–Lebesgue lemma using this transform. Furthermore, we formulate two important inequalities (sharp Pitt’s and sharp Hausdorff–Young’s inequalities) and three main uncertainty principles (logarithmic, Donoho–Stark’s, and Heisenberg’s uncertainty principles) for the OQPFT. To complete our investigation, we construct three elementary examples of signal theory with graphical interpretations to illustrate the use of OQPFT and discuss their particular cases.
Journal Article
5-mC methylation study of sORFs in 3ˈUTR of transcription factor JUNGBRUNNEN 1-like during leaf rust pathogenesis in wheat
Background
JUB1, a NAC domain containing hydrogen peroxide-induced transcription factor, plays a critical role in plant immunity. Little is known about how
JUB1
responds to leaf rust disease in wheat. Recent discoveries in genomics have also unveiled a multitude of sORFs often assumed to be non-functional, to argue for the necessity of including them as potential regulatory players of translation. However, whether methylation on sORFs spanning the 3’UTR of regulatory genes like
JUB1
modulate gene expression, remains unclear.
Methods and results
In this study, we identified the methylation states of two sORFs in 3’UTR of a homologous gene of
JUB1
in wheat,
TaJUB1-L
, at cytosine residues in CpG, CHH and CHG sites at different time points of disease progression in two near-isogenic lines of wheat (HD2329), with and without
Lr24
gene during leaf rust pathogenesis. Here, we report a significant demethylation of the CpG dinucleotides occurring in the sORFs of the 3’UTR in the resistant isolines after 24 h post-infection. Also, the up-regulated gene expression observed through RT-qPCR was directly proportional to the demethylation of the CpG sites in the sORFs.
Conclusions
Our findings indicate that
TaJUB1-L
might be a positive regulator in providing tolerance during leaf rust pathogenesis and cytosine methylation at 3’UTR might act as a switch for its expression control. These results enrich the potential benefit of conventional methylation assay techniques for unraveling the unexplored enigma in epigenetics during plant-pathogen interaction in a cost-effective and confidentially conclusive manner.
Journal Article
Statistical Analysis of Long Term Trends of Rainfall During 1901–2002 at Assam, India
Rainfall is a principal element of the hydrological cycle and its variability is important from both the scientific as well as socio-economic point of view. This study presents an analysis based on the precipitation variation in Assam, India over 102 years from 1901 to 2002. Precipitation data from 21 stations have been collected. These data have been analyzed for both annual and seasonal variation. For trend analysis, Mann-Kendell and Sen’s slope estimator test were used. To compare seasonal variations, three seasons of winter, summer and monsoon have been considered. Mean annual precipitation varied from 2,074 mm (at Tinsukia) to 3,538 mm (at North Chahar Hills). The most probable year of change was 1959 in annual precipitation. Time series of the Standardized Precipitation Index (SPI) depict that near normal occurs in about 68 years out 102 years, and in 2.48 years out of 102 years there was an extreme wet. All these findings can help provide rational regulatory and policy in relation to water resources to maintain the health of the various ecosystems that make up Assam, India.
Journal Article